{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"marker","slug":"marker","name":"Marker","type":"repo","url":"https://github.com/VikParuchuri/marker","page_url":"https://unfragile.ai/marker","categories":["data-pipelines"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"marker__cap_0","uri":"capability://data.processing.analysis.multi.format.document.ingestion.with.provider.abstraction","name":"multi-format document ingestion with provider abstraction","description":"Converts PDF, PowerPoint, Word, Excel, EPUB, and image files into a unified internal document representation through a pluggable provider architecture. Each provider handles format-specific extraction (e.g., PDF uses pdfplumber or PyPDF2, Office formats use python-pptx/python-docx), normalizing diverse input types into a common block-based schema for downstream processing. The provider pattern enables extensibility without modifying core pipeline logic.","intents":["I need to process documents in multiple formats (PDF, DOCX, PPTX) through a single pipeline","I want to add support for a custom document format without rewriting the entire converter","I need to extract raw content from various sources before layout analysis"],"best_for":["Teams building document processing pipelines that must handle heterogeneous input formats","Developers extending Marker with proprietary or specialized document types","RAG systems that ingest documents from multiple sources"],"limitations":["Provider implementations vary in fidelity — some formats lose layout information during conversion to PDF intermediate representation","Office format extraction depends on external libraries (python-pptx, python-docx) which may not preserve all formatting","Image-based documents require OCR fallback, adding latency and potential accuracy loss"],"requires":["Python 3.9+","pdfplumber or PyPDF2 for PDF extraction","python-pptx for PowerPoint support","python-docx for Word document support","openpyxl for Excel support"],"input_types":["PDF files","PowerPoint presentations (.pptx)","Word documents (.docx)","Excel spreadsheets (.xlsx)","EPUB ebooks","Image files (PNG, JPG, TIFF)"],"output_types":["Unified internal Document object with hierarchical block structure","Normalized page and block metadata with spatial coordinates"],"categories":["data-processing-analysis","document-conversion"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"marker__cap_1","uri":"capability://image.visual.deep.learning.based.layout.detection.and.spatial.analysis","name":"deep learning-based layout detection and spatial analysis","description":"Uses pre-trained deep learning models (via detectron2 or similar vision transformers) to identify document structure elements (text regions, tables, figures, headers, footers) and their spatial relationships through polygon-based bounding box detection. The layout builder constructs a hierarchical block tree that preserves 2D positioning information, enabling accurate reconstruction of document structure even in complex multi-column or non-linear layouts. This approach outperforms rule-based heuristics for varied document designs.","intents":["I need to accurately detect document structure in PDFs with complex layouts (multi-column, sidebars, floating elements)","I want to preserve spatial relationships between elements for downstream processing","I need to distinguish headers, footers, and page artifacts from main content"],"best_for":["Processing academic papers, technical documentation, and complex business reports with non-standard layouts","Teams requiring high-fidelity document structure preservation for LLM-based analysis","Applications where layout-aware rendering is critical (e.g., preserving column structure)"],"limitations":["Deep learning models require GPU acceleration for reasonable throughput; CPU processing is 5-10x slower","Models are trained on specific document types; performance degrades on unusual layouts (e.g., handwritten annotations, scanned documents with skew)","Polygon-based coordinates are relative to page dimensions; requires careful handling for documents with variable page sizes","Model inference adds 200-500ms per page latency depending on hardware"],"requires":["Python 3.9+","PyTorch 2.0+ with CUDA 11.8+ for GPU support (or CPU fallback)","detectron2 or equivalent vision model library","GPU with 4GB+ VRAM recommended for batch processing"],"input_types":["Rasterized PDF pages (as images)","Document page images (PNG, JPG)"],"output_types":["Polygon coordinates for detected regions","Block hierarchy with spatial metadata (x, y, width, height, rotation)","Block type classifications (text, table, figure, header, footer)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"marker__cap_10","uri":"capability://automation.workflow.batch.document.processing.with.multi.gpu.acceleration","name":"batch document processing with multi-gpu acceleration","description":"Processes multiple documents in parallel using a configurable batch pipeline that distributes work across available GPUs or CPU cores. Implements job queuing, progress tracking, and error handling for large-scale document conversion. Supports distributed processing via Python multiprocessing or async I/O, with configurable batch sizes and worker counts. Enables efficient processing of document collections for RAG systems or data extraction pipelines.","intents":["I need to convert thousands of PDFs to Markdown for a RAG system efficiently","I want to distribute document processing across multiple GPUs to maximize throughput","I need progress tracking and error handling for large batch jobs"],"best_for":["Teams processing large document collections (100s-1000s of files) for RAG systems or data extraction","Organizations with multi-GPU infrastructure looking to maximize throughput","Applications requiring batch processing with progress monitoring and error recovery"],"limitations":["Multi-GPU processing requires careful memory management; OOM errors possible with large documents or small GPU memory","Batch processing adds complexity; debugging failures in parallel jobs is harder than single-document processing","No built-in distributed processing across multiple machines; limited to single-machine parallelism","Progress tracking and checkpointing require external state management (database, file system)","Error handling in one document doesn't automatically skip to next; requires custom error recovery logic"],"requires":["Python 3.9+","PyTorch with CUDA support for multi-GPU processing","Multiple GPUs (optional but recommended) or multi-core CPU","Sufficient disk space for output files","Optional: job queue system (Celery, RQ) for distributed processing"],"input_types":["Directory of PDF or other document files","List of file paths or document objects","Configuration for batch size, worker count, GPU allocation"],"output_types":["Converted documents in specified format (Markdown, JSON, HTML)","Progress log with per-document status","Error report with failed documents and reasons","Metadata about processing (duration, GPU utilization, throughput)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"marker__cap_11","uri":"capability://tool.use.integration.configuration.system.with.environment.based.overrides.and.component.discovery","name":"configuration system with environment-based overrides and component discovery","description":"Provides a centralized configuration system that manages model selection, processing options, LLM provider credentials, and output format settings. Supports environment variable overrides for deployment flexibility, YAML/JSON configuration files for complex setups, and dynamic component discovery via entry points. Enables users to customize behavior (e.g., which layout model to use, OCR provider, LLM service) without code changes.","intents":["I need to configure Marker for different environments (dev, staging, production) without code changes","I want to switch between different LLM providers or layout models based on configuration","I need to manage API credentials securely using environment variables"],"best_for":["Teams deploying Marker in multiple environments with different configurations","Organizations requiring flexible model selection (e.g., switching between OpenAI and local LLMs)","DevOps teams managing Marker as part of larger document processing infrastructure"],"limitations":["Configuration complexity increases with number of options; no built-in validation of configuration values","Environment variable overrides can be error-prone if variable names are not well-documented","No built-in configuration versioning or rollback; changes to config files are not tracked","Component discovery via entry points requires understanding of Python packaging; custom components need proper setup.py configuration"],"requires":["Python 3.9+","Understanding of environment variables and configuration file formats (YAML/JSON)","For custom components: knowledge of Python entry points and Marker's component interfaces"],"input_types":["Environment variables","Configuration files (YAML, JSON)","Command-line arguments","Python configuration objects"],"output_types":["Resolved configuration with all overrides applied","Loaded component instances (models, providers, renderers)","Configuration metadata for debugging"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"marker__cap_12","uri":"capability://tool.use.integration.web.api.server.with.rest.endpoints.for.document.conversion","name":"web api server with rest endpoints for document conversion","description":"Provides a REST API server (FastAPI-based) that exposes document conversion as HTTP endpoints, enabling integration with external systems and web applications. Supports file upload, conversion with configurable options, and streaming output. Implements request queuing, timeout handling, and resource limits to prevent abuse. Enables Marker to be deployed as a microservice for document processing pipelines.","intents":["I need to integrate document conversion into a web application or microservice architecture","I want to expose Marker's capabilities via REST API for use by non-Python applications","I need to handle concurrent conversion requests with proper queuing and resource management"],"best_for":["Teams building document processing microservices or APIs","Web applications requiring server-side document conversion","Organizations integrating Marker into larger systems via REST endpoints"],"limitations":["REST API adds network latency compared to direct Python library usage","File upload size limits and timeout handling require careful configuration","No built-in authentication or rate limiting; requires external API gateway for production","Streaming large outputs (e.g., multi-page documents) can consume significant memory","No built-in caching; repeated conversions of same document are not deduplicated"],"requires":["Python 3.9+","FastAPI and Uvicorn for API server","Network connectivity for API clients","Sufficient memory and CPU for concurrent requests"],"input_types":["HTTP POST requests with file upload","Query parameters for conversion options (output format, LLM enhancement, etc.)","JSON request body with configuration"],"output_types":["HTTP response with converted document (Markdown, JSON, HTML)","Streaming response for large documents","Error responses with detailed error messages"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"marker__cap_13","uri":"capability://data.processing.analysis.form.field.detection.and.data.extraction.with.structured.output","name":"form field detection and data extraction with structured output","description":"Detects form fields (text inputs, checkboxes, radio buttons, dropdowns) using layout analysis and specialized form processors. Extracts field values and metadata (field name, type, position, default value) and outputs structured data (JSON, CSV) suitable for downstream processing. Supports both filled and unfilled forms, with optional LLM-based field value correction for low-confidence extractions.","intents":["I need to extract data from filled PDF forms and convert to structured format (JSON, CSV)","I want to identify form fields and their types for automated data entry or validation","I need to handle complex forms with conditional fields or nested structures"],"best_for":["Processing business forms, surveys, and applications for data extraction","Teams automating form data entry or validation workflows","Applications requiring structured extraction from form-heavy documents"],"limitations":["Form field detection relies on layout analysis; unusual form designs may not be recognized","Filled form values extracted via OCR have accuracy limitations (70-90% depending on handwriting quality)","No support for complex form logic (conditional fields, dynamic sections)","Nested or multi-page forms may not be handled correctly","LLM-based correction adds latency and cost; not suitable for real-time processing"],"requires":["Python 3.9+","Layout detection models for form field identification","OCR engine for extracting filled values","Optional: LLM API for field value correction"],"input_types":["PDF forms (filled or unfilled)","Detected form regions from layout analysis","Form field metadata (position, type, label)"],"output_types":["JSON with extracted form data and field metadata","CSV export of form values","Structured form schema with field definitions","Confidence scores for extracted values"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"marker__cap_2","uri":"capability://image.visual.ocr.and.text.line.detection.with.fallback.mechanisms","name":"ocr and text line detection with fallback mechanisms","description":"Performs optical character recognition (OCR) on document regions where native text extraction fails, using Tesseract or cloud-based OCR APIs as fallback. Integrates text line detection models to identify individual text lines and their bounding boxes, enabling character-level positioning for accurate reconstruction. The system automatically routes content through OCR when PDF text extraction yields low confidence or when processing scanned/image-based documents, with configurable confidence thresholds.","intents":["I need to extract text from scanned PDFs or image-based documents where native text extraction is unavailable","I want to preserve text positioning information for layout-aware reconstruction","I need fallback mechanisms for documents with mixed native and scanned content"],"best_for":["Processing scanned documents, historical archives, and image-heavy PDFs","Applications requiring character-level positioning for precise layout reconstruction","Teams processing documents in multiple languages where OCR accuracy is critical"],"limitations":["OCR accuracy degrades significantly on low-quality scans, handwriting, or non-Latin scripts (varies 60-95% depending on image quality)","Tesseract OCR adds 500ms-2s per page; cloud APIs add network latency and per-request costs","Text line detection models may miss overlapping text or unusual typography","No built-in spell-checking or post-OCR correction; garbage input produces garbage output"],"requires":["Python 3.9+","Tesseract OCR engine (system dependency) or cloud OCR API credentials (Google Vision, Azure, AWS Textract)","pytesseract or cloud SDK for API integration","For cloud OCR: API key and billing setup"],"input_types":["Rasterized PDF pages (images)","Scanned document images (PNG, JPG, TIFF)","Low-confidence text regions from native PDF extraction"],"output_types":["Extracted text with confidence scores","Text line bounding boxes and character-level positioning","Language detection metadata"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"marker__cap_3","uri":"capability://data.processing.analysis.structured.table.extraction.and.reconstruction.with.llm.enhancement","name":"structured table extraction and reconstruction with llm enhancement","description":"Detects table regions via layout analysis, extracts cell content through OCR or native text extraction, and reconstructs table structure (rows, columns, merged cells) using heuristic-based cell alignment and optional LLM-based refinement. The table processor handles complex tables with merged cells, nested headers, and irregular layouts by analyzing cell boundaries and content relationships. LLM processors can be invoked to correct misaligned cells or infer missing content, trading latency for accuracy.","intents":["I need to extract tables from PDFs and convert them to Markdown or structured formats","I want to handle complex tables with merged cells and irregular layouts","I need to improve table accuracy for documents where heuristic extraction fails"],"best_for":["Processing financial reports, data sheets, and technical documentation with tabular data","Teams requiring high-fidelity table reconstruction for downstream data analysis","Applications where table accuracy directly impacts data quality (e.g., data extraction pipelines)"],"limitations":["Heuristic-based cell alignment fails on tables with complex merged cells or non-uniform spacing","LLM enhancement adds 1-5s per table depending on model and table complexity","No support for nested tables or tables spanning multiple pages","Rotated or skewed tables may not be detected or extracted correctly","Sparse tables with large whitespace may be misinterpreted as separate tables"],"requires":["Python 3.9+","Layout detection models (detectron2 or equivalent)","For LLM enhancement: OpenAI API key, Anthropic key, or local LLM endpoint","GPU recommended for layout detection; CPU fallback available but slow"],"input_types":["Detected table regions from layout analysis","Cell content from OCR or native text extraction","Table boundary polygons"],"output_types":["Markdown table format","JSON structured table representation","HTML table markup","CSV export"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"marker__cap_4","uri":"capability://data.processing.analysis.equation.and.mathematical.notation.recognition","name":"equation and mathematical notation recognition","description":"Detects mathematical expressions (both inline and display equations) using layout analysis and specialized processors that convert LaTeX, MathML, or image-based equations into Markdown-compatible notation (e.g., `$...$` for inline, `$$...$$` for display). Handles both native PDF equations and image-based math through OCR fallback. The system preserves equation positioning and context within document flow.","intents":["I need to extract equations from scientific papers and technical documents while preserving LaTeX formatting","I want to convert image-based equations to text-based notation for LLM processing","I need to distinguish inline math from display equations for proper Markdown rendering"],"best_for":["Processing academic papers, textbooks, and technical documentation with heavy mathematical content","Teams building RAG systems for scientific literature that must preserve equation semantics","Applications requiring equation extraction for downstream symbolic computation or analysis"],"limitations":["Image-based equation recognition via OCR has 70-85% accuracy depending on equation complexity and image quality","Complex multi-line equations or matrices may be incorrectly segmented","No support for custom notation or domain-specific mathematical symbols","Equation detection relies on layout analysis; equations in unusual positions may be missed","No validation that extracted LaTeX is syntactically correct"],"requires":["Python 3.9+","Layout detection models for equation region identification","Tesseract or cloud OCR for image-based equation recognition","Optional: pix2tex or similar models for handwritten equation recognition"],"input_types":["Native PDF equations (MathML, LaTeX embedded in PDF)","Equation images (PNG, JPG)","Detected equation regions from layout analysis"],"output_types":["LaTeX notation (e.g., `$x^2 + y^2 = z^2$`)","MathML representation","Markdown-compatible equation syntax","Equation metadata (position, type: inline/display)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"marker__cap_5","uri":"capability://image.visual.image.extraction.and.preservation.with.metadata.tracking","name":"image extraction and preservation with metadata tracking","description":"Detects and extracts images from documents, preserving them as separate files with configurable formats (PNG, JPG, WebP) and resolution. Tracks image metadata (position, size, caption, alt-text) and maintains references in output Markdown/JSON, enabling downstream processing or LLM-based image description. Supports batch image extraction with deduplication to avoid storing identical images multiple times.","intents":["I need to extract all images from a PDF and save them as separate files with proper naming","I want to preserve image references in Markdown output with alt-text and captions","I need to generate descriptions for images using LLMs for accessibility or RAG indexing"],"best_for":["Processing visually-rich documents (reports, presentations, technical documentation) for RAG systems","Teams requiring image extraction for accessibility compliance (alt-text generation)","Applications needing to preserve document images for downstream computer vision analysis"],"limitations":["Image extraction from PDFs may yield low-quality rasterized versions if original images are embedded at low resolution","No automatic caption or alt-text generation without LLM integration","Image deduplication relies on hash comparison; visually similar but slightly different images are treated as distinct","Large documents with many high-resolution images can consume significant disk space","Embedded images in tables or complex layouts may be incorrectly positioned or missed"],"requires":["Python 3.9+","PIL/Pillow for image processing","Optional: LLM API credentials for image description generation (OpenAI Vision, Claude, etc.)"],"input_types":["Detected image regions from layout analysis","Embedded images in PDF or Office documents","Rasterized document pages"],"output_types":["Extracted image files (PNG, JPG, WebP)","Image metadata (position, dimensions, caption, alt-text)","Markdown image references with alt-text","JSON image inventory with paths and metadata"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"marker__cap_6","uri":"capability://data.processing.analysis.header.footer.and.artifact.removal.with.configurable.heuristics","name":"header, footer, and artifact removal with configurable heuristics","description":"Identifies and removes repetitive page elements (headers, footers, page numbers, watermarks) using spatial analysis and content matching heuristics. The system detects elements that appear on multiple pages in similar positions, marks them as artifacts, and excludes them from output. Configurable thresholds allow tuning sensitivity to balance between removing true artifacts and preserving legitimate content that happens to repeat.","intents":["I need to remove headers and footers from PDFs before feeding them to LLMs","I want to eliminate page numbers and watermarks that clutter the output","I need to preserve legitimate repeated content (e.g., section headers) while removing artifacts"],"best_for":["Processing multi-page documents for RAG systems where headers/footers add noise","Teams preparing documents for LLM ingestion where artifact removal improves token efficiency","Applications requiring clean text extraction without page-level metadata"],"limitations":["Heuristic-based detection may incorrectly classify legitimate repeated content as artifacts (e.g., section headers that appear on multiple pages)","Configurable thresholds require manual tuning per document type; no one-size-fits-all setting","Artifacts with variable positioning or content (e.g., dynamic page numbers) may not be detected","No support for removing artifacts that span multiple pages or are embedded in text blocks"],"requires":["Python 3.9+","Layout detection models for spatial analysis","Configuration parameters for artifact detection sensitivity"],"input_types":["Extracted text blocks with spatial metadata","Page-level content from layout analysis"],"output_types":["Filtered text blocks with artifacts removed","Metadata indicating which blocks were classified as artifacts","Cleaned Markdown/JSON output"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"marker__cap_7","uri":"capability://data.processing.analysis.hierarchical.block.based.document.schema.with.spatial.indexing","name":"hierarchical block-based document schema with spatial indexing","description":"Represents documents as a tree of nested blocks (pages, paragraphs, text lines, tables, figures) with spatial metadata (polygon coordinates, bounding boxes, rotation). Each block tracks its type, content, and relationships to parent/sibling blocks, enabling efficient querying and processing of specific element types. The schema supports multiple extraction methods per block type and enables spatial indexing for fast region-based lookups.","intents":["I need to query document structure programmatically (e.g., find all tables on page 5)","I want to preserve spatial relationships between elements for layout-aware processing","I need to support multiple extraction strategies per block type (native text vs. OCR)"],"best_for":["Developers building custom document processing pipelines that need fine-grained control over block handling","Teams implementing layout-aware rendering or spatial analysis on top of Marker","Applications requiring efficient querying of document structure (e.g., finding all figures in a section)"],"limitations":["Hierarchical schema adds memory overhead compared to flat text representation (~10-20% for typical documents)","Spatial indexing requires careful handling of coordinate systems across different page sizes and rotations","No built-in persistence; requires external serialization (JSON, database) for storage","Schema evolution requires careful versioning to maintain backward compatibility"],"requires":["Python 3.9+","Understanding of Marker's block schema and spatial coordinate systems","For persistence: JSON serialization or database ORM"],"input_types":["Extracted content from providers and builders","Spatial metadata from layout analysis"],"output_types":["Hierarchical Document object with nested Block tree","JSON representation of document structure","Spatial index for region-based queries"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"marker__cap_8","uri":"capability://planning.reasoning.llm.powered.content.refinement.with.parallel.processing","name":"llm-powered content refinement with parallel processing","description":"Optionally invokes Large Language Models (OpenAI, Anthropic, local models) to refine extracted content, correct OCR errors, improve table structure, generate image descriptions, or fix complex formatting. Implements parallel LLM processing to handle multiple blocks concurrently, with configurable batch sizes and rate limiting. Supports specialized LLM processors for different content types (tables, forms, handwriting, complex layouts), enabling targeted accuracy improvements without processing entire documents through LLMs.","intents":["I need to improve OCR accuracy on low-quality scans by having an LLM correct obvious errors","I want to generate descriptions for images in documents for accessibility or RAG indexing","I need to fix misaligned tables or extract data from complex forms using LLM reasoning"],"best_for":["Teams prioritizing accuracy over speed, willing to pay for LLM API calls","Processing documents with known extraction challenges (scanned PDFs, complex tables, handwriting)","Applications where content quality directly impacts downstream tasks (data extraction, RAG indexing)"],"limitations":["LLM refinement adds 1-10s per block depending on model and content complexity; not suitable for real-time processing","Requires API credentials and billing for cloud LLMs; local models require GPU and significant memory","LLM outputs may hallucinate or introduce errors; no guarantee of correctness","Parallel processing adds complexity; rate limiting required to avoid API throttling","Cost scales with document size and number of blocks processed; can become expensive for large batches"],"requires":["Python 3.9+","API credentials for OpenAI, Anthropic, or other LLM provider","For local LLMs: GPU with 8GB+ VRAM, Ollama or similar inference engine","Network connectivity for cloud LLM APIs"],"input_types":["Extracted text blocks with low confidence scores","Detected tables with alignment issues","Image regions for description generation","Form fields with uncertain values"],"output_types":["Refined text with corrections applied","Improved table structure","Generated image descriptions","Extracted form data with confidence scores"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"marker__cap_9","uri":"capability://data.processing.analysis.multi.format.output.rendering.with.configurable.serialization","name":"multi-format output rendering with configurable serialization","description":"Renders processed documents to multiple output formats (Markdown, JSON, HTML) with configurable options for each format. The renderer system is pluggable, allowing custom renderers for domain-specific formats. Markdown output preserves structure through heading levels, lists, and code blocks; JSON output includes full metadata and spatial information; HTML output enables web-based viewing. Each renderer can be configured to include/exclude specific elements (images, tables, equations, metadata).","intents":["I need to convert PDFs to Markdown for use in LLM pipelines and documentation systems","I want to export documents as JSON with full metadata for downstream processing","I need to generate HTML output for web viewing while preserving document structure"],"best_for":["Teams building document processing pipelines that need multiple output formats","Developers integrating Marker into RAG systems (Markdown output) or data extraction pipelines (JSON output)","Applications requiring web-based document viewing with preserved structure (HTML output)"],"limitations":["Markdown output loses some formatting (e.g., text color, font styles) that don't map to Markdown syntax","JSON output can be verbose for large documents; requires careful handling of nested structures","HTML output requires CSS for proper styling; no built-in CSS provided","Custom renderers require understanding of Marker's block schema and output format requirements","No support for output formats beyond Markdown, JSON, HTML without custom renderer implementation"],"requires":["Python 3.9+","Processed Document object from pipeline","For custom renderers: understanding of renderer interface and block schema"],"input_types":["Processed Document object with hierarchical block structure","Block metadata (type, content, spatial info, extracted elements)"],"output_types":["Markdown files (.md) with structure preserved","JSON files with full metadata and spatial information","HTML files with semantic markup","Custom formats via pluggable renderers"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"marker__headline","uri":"capability://data.processing.analysis.high.performance.document.conversion.system","name":"high-performance document conversion system","description":"Marker is a fast and accurate PDF to Markdown converter that utilizes deep learning for layout detection and OCR, making it ideal for transforming various document formats into structured text for LLM pipelines.","intents":["best PDF to Markdown converter","document conversion tool for LLM integration","high-performance document processing system","PDF extraction and formatting solution","convert PDF to Markdown with OCR"],"best_for":["developers needing accurate document conversion","users looking to feed documents into LLMs"],"limitations":[],"requires":[],"input_types":["PDF","Word","Excel","PowerPoint","EPUB","images"],"output_types":["Markdown","JSON","HTML"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":55,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","pdfplumber or PyPDF2 for PDF extraction","python-pptx for PowerPoint support","python-docx for Word document support","openpyxl for Excel support","PyTorch 2.0+ with CUDA 11.8+ for GPU support (or CPU fallback)","detectron2 or equivalent vision model library","GPU with 4GB+ VRAM recommended for batch processing","PyTorch with CUDA support for multi-GPU processing","Multiple GPUs (optional but recommended) or multi-core CPU"],"failure_modes":["Provider implementations vary in fidelity — some formats lose layout information during conversion to PDF intermediate representation","Office format extraction depends on external libraries (python-pptx, python-docx) which may not preserve all formatting","Image-based documents require OCR fallback, adding latency and potential accuracy loss","Deep learning models require GPU acceleration for reasonable throughput; CPU processing is 5-10x slower","Models are trained on specific document types; performance degrades on unusual layouts (e.g., handwritten annotations, scanned documents with skew)","Polygon-based coordinates are relative to page dimensions; requires careful handling for documents with variable page sizes","Model inference adds 200-500ms per page latency depending on hardware","Multi-GPU processing requires careful memory management; OOM errors possible with large documents or small GPU memory","Batch processing adds complexity; debugging failures in parallel jobs is harder than single-document processing","No built-in distributed processing across multiple machines; limited to single-machine parallelism","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.693Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=marker","compare_url":"https://unfragile.ai/compare?artifact=marker"}},"signature":"Fb7XZpyUePubLH2fL02QUYY9nsGJL99x4eVudK4qPh9J1xH9UkAXC8jE9L7aMeSZs/rR1CLl46UoHpxhUH4tCQ==","signedAt":"2026-06-20T18:38:37.806Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/marker","artifact":"https://unfragile.ai/marker","verify":"https://unfragile.ai/api/v1/verify?slug=marker","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}